In the study comparing lorazepam to olanzapine, olanzapine was superior to lorazepam, and both were superior to placebo. As expected, the safety of lorazepam among the different studies was consistent with its well-characterized profile with dizziness, sedation, and somnolence being the most common adverse events. Based on this structured review, lorazepam can be considered to be a clinically effective means of treating the acutely agitated patient.Background Less than 20% of people with addictions have access to adequate treatment. Mobile health could improve access to care. No systematic review evaluates effectiveness of mobile health applications for addiction. Objectives First aim was to describe controlled trials evaluating the effectiveness of smartphone applications targeting substance use disorders and addictive behaviors. Secondly, we aimed to understand how the application produced changes in behavior and craving management. Method A systematic review based on PRISMA recommendations was conducted on MEDLINE, CENTRAL, and PsycINFO. https://www.selleckchem.com/products/Trichostatin-A.html Studies had to be controlled trials concerning addictive disorders (substance/behavior), mobile application-based interventions, assessing effectiveness or impact of those applications upon use, published after 2008. Relevant information was systematically screened for synthesis. Quality and risk of bias were evaluated with JADAD score. Results Search strategy retrieved 22 articles (2014-2019) corresponding to 22 applications targeting tobacco, alcohol, other substances and binge eating disorder. Control groups had access to usual treatments or a placebo-application or no treatment. Eight applications showed reduced use. Most of the applications informed about risks of use and suggestions for monitoring use. Twelve applications managed craving. Discussion Heterogeneity limited study comparisons. Duration of studies was too short to predict sustainable results. A reduction of craving seemed related to a reduction in use. Conclusion There is a lack of robust and comparable studies on mHealth applications for addiction treatment. Such applications could become significant contributors in clinical practice in the future so longer-termed double-blind studies are needed. Targeting craving to prevent relapse should be systematic.Introduction Drug checking as a part of drug harm-reduction strategies represents an essential aspect of public health policies. It focuses on rapid identification of drugs that individuals intend to use during night events, in order to implement health-protective behaviors. Chemical drug analysis techniques vary considerably, from simple colorimetric reagents to advanced forensic methods such as gas chromatography/mass spectrometry (GC/MS). Materials and Methods In 2019, drug-check services were offered at some night events in Umbria (Central Italy). One hundred and twenty attendees directly delivered unidentified substances to a harm-reduction worker, who collected a few milligrams of the substances on ceramic plates and added a drop of colorimetric reagent. Multiple reagents were used to increase the diagnostic capacity of a substance, which may react with a specific drug or a few drugs. Later, a fraction of the samples was analyzed by GC/MS. The concordance of the results obtained using these two methodolLSD) or minimal in quantity, but failed to identify mixtures of substances and the adulterants present in them. Therefore, the use of more discriminatory on-site methods such as Raman or infrared spectrometry is strongly recommended.Background Cannabidiol (CBD) is a cannabinoid of potential interest for the treatment of substance use disorders. Our aim was to review the outcome measures, surrogate endpoints, and biomarkers in published and ongoing randomized clinical trials. Methods We conducted a search in PubMed, Web of Science, PMC, PsycINFO, EMBASE, CENTRAL Cochrane Library, "clinicalTrials.gov," "clinicaltrialsregister.eu," and "anzctr.org.au" for published and ongoing studies. Inclusion criteria were randomized clinical trials (RCTs) examining the use of CBD alone or in association with other cannabinoids, in all substance use disorders. The included studies were analyzed in detail and their qualities assessed by a standardized tool (CONSORT 2010). A short description of excluded studies, consisting in controlled short-term or single administration in non-treatment-seeking drug users, is provided. Findings The screening retrieved 207 published studies, including only 3 RCTs in cannabis use disorder. Furthermore, 12 excluded studies in cannabis, tobacco, and opioid use disorders are described. Interpretation Primary outcomes were validated withdrawal symptoms scales and drug use reduction in the three RCTs. In the short-term or crossover studies, the outcome measures were visual analog scales for subjective states; self-rated scales for withdrawal, craving, anxiety, or psychotomimetic symptoms; and laboratory tasks of drug-induced craving, effort expenditure, attentional bias for substance, impulsivity, or anxiety to serve as surrogate endpoints for treatment efficacy. Of note, ongoing studies are now adding peripheral biomarkers of the endocannabinoid system status to predict treatment response. Conclusion The outcome measures and biomarkers assessed in the ongoing CBD trials for substance use disorders are improving.Objective Multiple relapses over time are common in both affective and non-affective psychotic disorders. Characterizing the temporal nature of these relapses may be crucial to understanding the underlying neurobiology of relapse. Materials and Methods Anonymized records of patients with affective and non-affective psychotic disorders were collected from SA Mental Health Data Universe and retrospectively analyzed. To characterize the temporal characteristic of their relapses, a relapse trend score was computed using a symbolic series-based approach. A higher score suggests that relapse follows a trend and a lower score suggests relapses are random. Regression models were built to investigate if this score was significantly different between affective and non-affective psychotic disorders. Results Logistic regression models showed a significant group difference in relapse trend score between the patient groups. For example, in patients who were hospitalized six or more times, relapse score in affective disorders were 2.